Magnetic Resonance Imaging (MRI) is considered today the golden-standard
modality for soft tissues. The long acquisition times, however, make it more
prone to motion artifacts as well as contribute to the relatively high costs of
this examination. Over the years, multiple studies concentrated on designing
reduced measurement schemes and image reconstruction schemes for MRI, however,
these problems have been so far addressed separately. On the other hand, recent
works in optical computational imaging have demonstrated growing success of the
simultaneous learning-based design of the acquisition and reconstruction
schemes manifesting significant improvement in the reconstruction quality with
a constrained time budget. Inspired by these successes, in this work, we
propose to learn accelerated MR acquisition schemes (in the form of Cartesian
trajectories) jointly with the image reconstruction operator. To this end, we
propose an algorithm for training the combined acquisition-reconstruction
pipeline end-to-end in a differentiable way. We demonstrate the significance of
using the learned Cartesian trajectories at different speed up rates.

In vivo measurements of muscle architecture (i.e. the spatial arrangement of
muscle fascicles) are routinely included in research and clinical settings to
monitor muscle structure, function and plasticity. However, in most cases such
measurements are performed manually, and more reliable and time-efficient
automated methods are either lacking completely, or are inaccessible to those
without expertise in image analysis. In this work, we propose an ImageJ script
to automate the entire analysis process of muscle architecture in ultrasound
images: Simple Muscle Architecture Analysis (SMA). Images are filtered in the
spatial and frequency domains with built-in commands and external plugins to
highlight aponeuroses and fascicles. Fascicle dominant orientation is then
computed in regions of interest using the OrientationJ plugin. Bland-Altman
plots of analyses performed manually or with SMA indicates that the automated
analysis does not induce any systematic bias and that both methods agree
equally through the range of measurements. Our test results illustrate the
suitability of SMA to analyse images from superficial muscles acquired with a
broad range of ultrasound settings.